AWS Big Data

OCTOBER 4, 2023

Business-driven domains – A DataZone domain represents the distinct boundary of a line of business (LOB) or a business area within an organization that can manage its own data, including its own data assets, its own definition of data or business terminology, and may have its own governing standards.

Metadata 100

Metadata Data Lake Publishing Data Governance 100

AWS Big Data

APRIL 19, 2023

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. We split the solution into two primary components: generating Spark job metadata and running the SQL on Amazon EMR. The script generates a metadata JSON file for each step.

Metadata 80

Metadata Testing Data Lake Consulting 80

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 103

Data Lake Management Metrics Data Warehouse 103

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

datapine

SEPTEMBER 29, 2022

Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. But first, let’s define what data quality actually is. What is the definition of data quality? Industry-wide, the positive ROI on quality data is well understood.

Data Quality 241

Data Quality Metrics Data-driven Management 241

AWS Big Data

NOVEMBER 15, 2023

It seamlessly consolidates data from various data sources within AWS, including AWS Cost Explorer (and forecasting with Cost Explorer ), AWS Trusted Advisor , and AWS Compute Optimizer. Data providers and consumers are the two fundamental users of a CDH dataset.

Dashboards 95

Dashboards Analytics Metadata Data Warehouse 95

AWS Big Data

FEBRUARY 21, 2023

Data warehouses play a vital role in healthcare decision-making and serve as a repository of historical data. A healthcare data warehouse can be a single source of truth for clinical quality control systems. What is a dimensional data model? What is a dimensional data model?

Data Warehouse 65

Data Warehouse Data Lake Cost-Benefit Metadata 65

AWS Big Data

DECEMBER 21, 2023

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

Data Lake 89

Data Lake Data Warehouse Data-driven B2B 89

AWS Big Data

SEPTEMBER 13, 2023

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker.

Data Lake 91

Data Lake Data Processing Metadata Snapshot 91

AWS Big Data

JANUARY 24, 2023

This solution only replicates metadata in the Data Catalog, not the actual underlying data. To have a redundant data lake using Lake Formation and AWS Glue in an additional Region, we recommend replicating the Amazon S3-based storage using S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication process.

Data Architecture 66

Data Architecture Metadata Data Lake Snapshot 66

AWS Big Data

MARCH 1, 2024

One of the bank’s key challenges related to strict cybersecurity requirements is to implement field level encryption for personally identifiable information (PII), Payment Card Industry (PCI), and data that is classified as high privacy risk (HPR). Only users with required permissions are allowed to access data in clear text.

Data Lake 99

Data Lake Data Warehouse Management Risk 99

erwin

OCTOBER 31, 2019

What data do we have and where is it? Data is a critical asset used to operate, manage and grow a business. While sometimes at rest in databases, data lakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed.

Data Governance 105

Data Governance Metadata Risk Data Lake 105

Jet Global

JULY 2, 2019

The sheer scale of data being captured by the modern enterprise has necessitated a monumental shift in how that data is stored. From the humble database through to data warehouses , data stores have grown both in scale and complexity to keep pace with the businesses they serve, and the data analysis now required to remain competitive.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

Alation

FEBRUARY 13, 2020

These are all good questions and a logical place to start your data cataloging journey. This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

AWS Big Data

MAY 24, 2023

Some of the important non-functional use cases for an S3 data lake that organizations are focusing on include storage cost optimizations, capabilities for disaster recovery and business continuity, cross-account and multi-Region access to the data lake, and handling increased Amazon S3 request rates. impl":"org.apache.iceberg.aws.s3.S3FileIO",

Data Lake 84

Data Lake Snapshot Metadata Optimization 84

Cloudera

MARCH 6, 2018

If catalog metadata and business definitions live with transient compute resources, they will be lost, requiring work to recreate later and making auditing impossible. Altus SDX includes a shared metadata catalog that puts data in context. Soon we’ll have Altus Data Science, too!

Analytics 40

Analytics Metadata Recreation/Entertainment Big Data 40

AWS Big Data

MARCH 11, 2024

Amazon Redshift is a fully managed, scalable cloud data warehouse that accelerates your time to insights with fast, straightforward, and secure analytics at scale. Tens of thousands of customers rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the most widely used cloud data warehouse.

Analytics 65

Analytics Data Warehouse Optimization Metrics 65

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

Domino Data Lab

APRIL 3, 2019

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). Definition and Descriptions. In other words, #adulting.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

MARCH 9, 2023

Thousands of customers rely on Amazon Redshift to build data warehouses to accelerate time to insights with fast, simple, and secure analytics at scale and analyze data from terabytes to petabytes by running complex analytical queries. Data loading is one of the key aspects of maintaining a data warehouse.

Slice and Dice 82

Slice and Dice Data Warehouse Metrics Metadata 82

Cloudera

NOVEMBER 13, 2017

As the market moves toward cloud-based big data and analytics, three qualities emerge as vital for success. The ability to discover and define metadata definitions for the business is a critical enabler for self-service functions. Make sure any cloud-based analytics service meets these criteria. We’re here to help.

Analytics 40

Analytics Big Data Machine Learning Cost-Benefit 40

AWS Big Data

MARCH 3, 2023

You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. Amazon EMR Serverless is a serverless option in Amazon EMR that makes it easy for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers.

Data Lake 83

Data Lake Dashboards Metrics Metadata 83

Domino Data Lab

JULY 22, 2019

There’s a really nice comfortable blend here of what’s important in business, in engineering, in data science, etc. I definitely want to provide some shout-outs. In data science, definitely, there are other people who’ve talked more about that and we’ll point to them.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

IBM Big Data Hub

OCTOBER 4, 2023

In fact, according in an IDC DataSphere study, IDC estimated that 10,628 exabytes (EB) of data was determined to be useful if analyzed, while only 5,063 exabytes (EB) of data (47.6%) was analyzed in 2022. How does an open data lakehouse architecture support AI? New insights and relationships are found in this combination.

Data Lake 78

Data Lake Metadata Cost-Benefit Data Warehouse 78

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. In the past, data movement was defined by ETL: extract, transform, and load.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

IBM Big Data Hub

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

Alation

JANUARY 25, 2022

Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. The data catalog is a foundational layer of the data fabric.

Metadata 52

Metadata IT Metrics Data-driven 52

Alation

APRIL 14, 2022

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. The Cloud Data Migration Challenge. It’s More Important to Know What Your Data Means Than Where It Is. Scheduling.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

erwin

SEPTEMBER 25, 2020

Therefore, the visual representation provided by a data model gives organizations the confidence to design their proposed systems and take them live. Data modeling is a critical component of metadata management , data governance and data intelligence. Automate data model and database schema generation.

Modeling 111

Modeling Cost-Benefit Visualization Metadata 111

Jet Global

MAY 1, 2023

Introduction Why should I read the definitive guide to embedded analytics? The Definitive Guide to Embedded Analytics is designed to answer any and all questions you have about the topic. It is now most definitely a need-to-have. These sit on top of data warehouses that are strictly governed by IT departments.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

IBM Big Data Hub

JULY 5, 2023

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data. Comprehensive data security and data governance (i.e.

Data Lake 60

Data Lake Metadata Cost-Benefit Data Warehouse 60